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New neural distance field models hand-object interactions

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced HaNDF, a novel object-conditioned neural distance field designed to model plausible hand-object configurations. This approach leverages Riemannian geometry to represent and project hand poses within the context of interacting objects. HaNDF is demonstrated to be effective for applications including pose refinement, reconstruction with occlusion, and synthesis of physically plausible manipulation. AI

IMPACT Introduces a new method for generating more realistic and context-aware hand poses in AI applications.

RANK_REASON Research paper published on arXiv detailing a new method for modeling hand-object interactions. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New neural distance field models hand-object interactions

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Research paper published on arXiv detailing a new method for modeling hand-object interactions. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zuhao Liu, Melih Darcan, Zhengdi Yu, Riza Alp Guler, Tolga Birdal ·

    HaNDF: Object-Conditioned Geometric Neural Hand Distance Fields

    arXiv:2611.08578v1 Announce Type: new Abstract: We propose, HaNDF, object-conditional neural distance fields as data-driven priors for modeling the plausible hand-object configurations, in the product manifold of hand articulations. While recent unconditional generative priors sh…

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